Andreas Holzinger“Augmenting human intelligence with artificial intelligence”

Assoc.Prof.Dr. Andreas HOLZINGER

PhD, MSc, MPh, BEng, CEng, DipEd, MBCS

Video Research Statement|Research 5p.|Teaching 5p.|CV 3p.

Holzinger Group HCI-KDD
Institute for Medical Informatics, Statistics & Documentation
Medical University Graz  and
Institute of Interactive Systems & Data Science
Graz University of Technology

Andreas Holzinger promotes a synergistic approach by integration of two areas to understand intelligence, enabling context-adaptive systems: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD). Andreas has pioneered in interactive machine learning (iML) with the human-in-the-loop. Andreas’ goal is to augment human intelligence with artificial intelligence to help to solve problems in health informatics.

Due to raising legal and privacy issues in the European Union glass box approaches will become important in the future to be able to make decisions transparent, re-traceable, thus understandable. Andreas’ aim is to explain why a machine decision has been made, paving the way towards explainable AI.

Andreas Holzinger is lead of the Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics at the Medical University Graz, and Associate Professor of Applied Computer Science at the Faculty of Computer Science and Biomedical Engineering at Graz University of Technology. He serves as consultant for the Canadian, US, UK, Swiss, French, Italian and Dutch governments, for the German Excellence Initiative, and as national expert in the European Commission. Andreas obtained a Ph.D. in Cognitive Science from Graz University in 1998 and his Habilitation (second Ph.D.) in Computer Science from Graz University of Technology in 2003. Andreas was Visiting Professor for Machine Learning & Knowledge Extraction in Verona, RWTH Aachen, University College London and Middlesex University London. Since 2016 Andreas is Visiting Professor for Machine Learning in Health Informatics at the Faculty of Informatics at Vienna University of Technology. He founded the Expert Network HCI-KDD to foster a synergistic combination of methodologies of two areas that offer ideal conditions toward unraveling problems in understanding intelligence: Human-Computer Interaction (HCI) & Knowledge Discovery/Data Mining (KDD), with the goal of augmenting human intelligence with artificial intelligence. Andreas is Associate Editor of Springer/Nature Knowledge and Information Systems (KAIS), Section Editor for Machine Learning of Springer/Nature BMC Medical Informatics and Decision Making (MIDM), and founding Editor-in-Chief of the cross-disciplinary journal Machine Learning & Knowledge Extraction (MAKE). He is organizer of the IFIP Cross-Domain Conference “Machine Learning & Knowledge Extraction (CD-MAKE)” and Austrian representative in the IFIP TC 12 Artificial Intelligence and member of IFIP WG 12.9 Computational Intelligence, the ACM, IEEE, GI, the Austrian Computer Science and the Association for the Advancement of Artificial Intelligence (AAAI). Since 2003 Andreas has participated in leading positions in 30+ R&D multi-national projects, budget 6+ MEUR, 300+ publications, 10k+ citations, h-Index = 45.

Subject: Data and Information Science
Technical Area: Machine Learning, Knowledge Extraction, Explainable Artificial Intelligence (ex-AI)
Application Area: Health Informatics

More information about the Holzinger Group: http://hci-kdd.org/

The goal of the Holzinger Group is to develop software which can learn from data to extract knowledge and improve with experience over time. However, the application of such automatic machine learning (aML) algorithms in complex domains (e.g. Health) seems elusive in the near future, and a good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems which are trivial for human learners. Consequently, interactive machine learning (iML) with a human-in-the-loop, thereby making use of human cognitive abilities, can be of particular interest to solve problems, where learning algorithms suffer due to insufficient training samples, where we deal with complex data and/or rare events or computationally hard problems, or where we need transparency, re-traceability, explainablity. A “doctor-in-the-loop” can help, and human expertise and long-term experience can assist in solving problems which otherwise would remain NP-hard.

However, successful MAchine learning & Knowledge Extraction (MAKE) for health informatics requires a deep understanding of the data ecosystem and a concerted effort cross-domain of 7 research topics; four main topics: 1) data integration, 2) learning algorithms, 3) visualization, 4) privacy, data protection, safety and security, and three special topics: 5) graphs, 6) topology, and 7) entropy. Please read the inaugural paper doi:10.3390/make1010001

MAKE-Video|MAKE-Journal|CD-MAKE

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